{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fba1eae1",
   "metadata": {},
   "source": [
    "实验环境信息等内容网址：\n",
    "\n",
    "repo: [Streamer0320/NLP-3b | GitHub](https://github.com/Streamer0320/NLP-3b/)\n",
    "\n",
    "fork: [jin-hao-0320/NLP-3b | Gitee](https://gitee.com/jin-hao-0320/NLP-3b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49c8569c",
   "metadata": {},
   "source": [
    "# 6. 学习分类文本\n",
    "\n",
    "模式识别是自然语言处理的一个核心部分。以-ed结尾的词往往是过去时态动词（[5.](https://usyiyi.github.io/nlp-py-2e-zh/5.html#chap-tag)）。频繁使用will是新闻文本的暗示（[3](https://usyiyi.github.io/nlp-py-2e-zh/3.html#chap-words)）。这些可观察到的模式——词的结构和词频——恰好与特定方面的含义关联，如时态和主题。但我们怎么知道从哪里开始寻找，形式的哪一方面关联含义的哪一方面？\n",
    "\n",
    "本章的目的是要回答下列问题：\n",
    "\n",
    "1. 我们怎样才能识别语言数据中能明显用于对其分类的特征？\n",
    "2. 我们怎样才能构建语言模型，用于自动执行语言处理任务？\n",
    "3. 从这些模型中我们可以学到哪些关于语言的知识？\n",
    "\n",
    "一路上，我们将研究一些重要的机器学习技术，包括决策树、朴素贝叶斯分类器和最大熵分类。我们会掩盖这些技术的数学和统计的基础，集中关注如何以及何时使用它们（更多的技术背景知识见进一步阅读一节）。在看这些方法之前，我们首先需要知道这个主题的范围十分广泛。\n",
    "\n",
    "<a href=\"#1-有监督分类\">1 有监督分类</a> \n",
    "\n",
    "<a href=\"#2-有监督分类的更多例子\">2 有监督分类的更多例子</a> \n",
    "\n",
    "<a href=\"#3-评估\">3 评估 </a> \n",
    "\n",
    "<a href=\"#4-决策树\">4 决策树</a> \n",
    "\n",
    "<a href=\"#5-朴素贝叶斯分类器\">5 朴素贝叶斯分类器</a> \n",
    "\n",
    "## 1 有监督分类\n",
    "\n",
    "分类是为给定的输入选择正确的类标签的任务。在基本的分类任务中，每个输入被认为是与所有其它输入隔离的，并且标签集是预先定义的。这里是分类任务的一些例子：\n",
    "\n",
    "- 判断一封电子邮件是否是垃圾邮件。\n",
    "- 从一个固定的主题领域列表中，如“体育”、“技术”和“政治”，决定新闻报道的主题是什么。\n",
    "- 决定词bank给定的出现是用来指河的坡岸、一个金融机构、向一边倾斜的动作还是在金融机构里的存储行为。\n",
    "\n",
    "基本的分类任务有许多有趣的变种。例如，在多类分类中，每个实例可以分配多个标签；在开放性分类中，标签集是事先没有定义的；在序列分类中，一个输入列表作为一个整体分类。\n",
    "\n",
    "一个分类称为有监督的，如果它的建立基于训练语料的每个输入包含正确标签。有监督分类使用的框架图如 [1.1](https://usyiyi.github.io/nlp-py-2e-zh/6.html#fig-supervised-classification)所示。\n",
    "\n",
    "![Images/supervised-classification.png](https://usyiyi.github.io/nlp-py-2e-zh/Images/fb1a02fe3607a0deb452086296fd6f69.jpg)\n",
    "\n",
    "图 1.1：有监督分类。（a）在训练过程中，特征提取器用来将每一个输入值转换为特征集。这些特征集捕捉每个输入中应被用于对其分类的基本信息，我们将在下一节中讨论它。特征集与标签的配对被送入机器学习算法，生成模型。（b）在预测过程中，相同的特征提取器被用来将未见过的输入转换为特征集。之后，这些特征集被送入模型产生预测标签。\n",
    "\n",
    "在本节的其余部分，我们将着眼于分类器如何能够解决各种各样的任务。我们讨论的目的不是要范围全面，而是给出在文本分类器的帮助下执行的任务的一个代表性的例子。\n",
    "\n",
    "**vscode jupyter TOC**\n",
    "\n",
    "<a href=\"#11-性别鉴定\">1.1 性别鉴定</a> \n",
    "\n",
    "<a href=\"#12-选择红正确的特正\">1.2 选择红正确的特正</a> \n",
    "\n",
    "<a href=\"#13-文档分类\">1.3 文档分类</a> \n",
    "\n",
    "<a href=\"#14-词性标注\">1.4 词性标注 </a> \n",
    "\n",
    "<a href=\"#15-探索上下文语境\">1.5 探索上下文语境</a>  \n",
    "\n",
    "<a href=\"#16-序列分类\">1.6 序列分类 </a> \n",
    "\n",
    "<a href=\"#17-其他序列分类方法\">1.7 其他序列分类方法</a> \n",
    "\n",
    "**jupyter notebook TOC**\n",
    "\n",
    "<a href=\"#1.1-性别鉴定\">1.1 性别鉴定</a> \n",
    "\n",
    "<a href=\"#1.2-选择红正确的特正\">1.2 选择红正确的特正</a> \n",
    "\n",
    "<a href=\"#1.3-文档分类\">1.3 文档分类</a> \n",
    "\n",
    "<a href=\"#1.4-词性标注\">1.4 词性标注 </a> \n",
    "\n",
    "<a href=\"#1.5-探索上下文语境\">1.5 探索上下文语境</a>  \n",
    "\n",
    "<a href=\"#1.6-序列分类\">1.6 序列分类 </a> \n",
    "\n",
    "<a href=\"#1.7-其他序列分类方法\">1.7 其他序列分类方法</a> \n",
    "\n",
    "\n",
    "## 1.1 性别鉴定\n",
    "\n",
    "在[4](https://usyiyi.github.io/nlp-py-2e-zh/2.html#sec-lexical-resources)中，我们看到，男性和女性的名字有一些鲜明的特点。以a，e和i结尾的很可能是女性，而以k，o，r，s和t结尾的很可能是男性。让我们建立一个分类器更精确地模拟这些差异。\n",
    "\n",
    "创建一个分类器的第一步是决定输入的什么样的特征是相关的，以及如何为那些特征编码。在这个例子中，我们一开始只是寻找一个给定的名称的最后一个字母。以下特征提取器函数建立一个字典，包含有关给定名称的相关信息："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "384a5e33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'last_letter': 'k'}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def gender_features(word):\n",
    "    return {'last_letter': word[-1]}\n",
    "gender_features('Shrek')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0047d032",
   "metadata": {},
   "source": [
    "这个函数返回的字典被称为特征集，映射特征名称到它们的值。特征名称是区分大小写的字符串，通常提供一个简短的人可读的特征描述，例如本例中的`'last_letter'`。特征值是简单类型的值，如布尔、数字和字符串。\n",
    "\n",
    "注意\n",
    "\n",
    "大多数分类方法要求特征使用简单的类型进行编码，如布尔类型、数字和字符串。但要注意仅仅因为一个特征是简单类型，并不一定意味着该特征的值易于表达或计算。的确，它可以用非常复杂的和有信息量的值作为特征，如第2个有监督分类器的输出。\n",
    "\n",
    "现在，我们已经定义了一个特征提取器，我们需要准备一个例子和对应类标签的列表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a054ef60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import names\n",
    "labeled_names = ([(name, 'male') for name in names.words('male.txt')] +\n",
    "[(name, 'female') for name in names.words('female.txt')])\n",
    "import random\n",
    "random.shuffle(labeled_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dbb6ebc",
   "metadata": {},
   "source": [
    "接下来，我们使用特征提取器处理`names`数据，并划分特征集的结果链表为一个训练集和一个测试集。训练集用于训练一个新的“朴素贝叶斯”分类器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "624f7b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "\n",
    "featuresets = [(gender_features(n), gender) for (n, gender) in labeled_names]\n",
    "train_set, test_set = featuresets[500:], featuresets[:500]\n",
    "classifier = nltk.NaiveBayesClassifier.train(train_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90935a75",
   "metadata": {},
   "source": [
    "在本章的后面，我们将学习更多关于朴素贝叶斯分类器的内容。现在，让我们只是在上面测试一些没有出现在训练数据中的名字："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ede781d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'male'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.classify(gender_features('Neo'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5ceee966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'female'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.classify(gender_features('Trinity'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d03127ab",
   "metadata": {},
   "source": [
    "请看*《黑客帝国》*中这些角色的名字被正确分类。尽管这部科幻电影的背景是在2199年，但它仍然符合我们有关名字和性别的预期。我们可以在大数据量的未见过的数据上系统地评估这个分类器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d12438f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.77\n"
     ]
    }
   ],
   "source": [
    "print(nltk.classify.accuracy(classifier, test_set))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6026e945",
   "metadata": {},
   "source": [
    "最后，我们可以检查分类器，确定哪些特征对于区分名字的性别是最有效的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d70bb13f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Informative Features\n",
      "             last_letter = 'a'            female : male   =     35.8 : 1.0\n",
      "             last_letter = 'k'              male : female =     31.4 : 1.0\n",
      "             last_letter = 'f'              male : female =     27.4 : 1.0\n",
      "             last_letter = 'p'              male : female =     11.8 : 1.0\n",
      "             last_letter = 'v'              male : female =     10.4 : 1.0\n"
     ]
    }
   ],
   "source": [
    "classifier.show_most_informative_features(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f16608e6",
   "metadata": {},
   "source": [
    "此列表显示训练集中以a 结尾的名字中女性是男性的38倍，而以k 结尾名字中男性是女性的31倍。这些比率称为似然比，可以用于比较不同特征-结果关系。\n",
    "\n",
    "注意\n",
    "\n",
    "**轮到你来：**修改`gender_features()`函数，为分类器提供名称的长度、它的第一个字母以及任何其他看起来可能有用的特征。用这些新特征重新训练分类器，并测试其准确性。\n",
    "\n",
    "在处理大型语料库时，构建一个包含每一个实例的特征的单独的列表会使用大量的内存。在这些情况下，使用函数`nltk.classify.apply_features`，返回一个行为像一个列表而不会在内存存储所有特征集的对象："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4703e9f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.classify import apply_features\n",
    "train_set = apply_features(gender_features, labeled_names[500:])\n",
    "test_set = apply_features(gender_features, labeled_names[:500])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4201f06",
   "metadata": {},
   "source": [
    "## 1.2 选择正确的特征\n",
    "\n",
    "选择相关的特征，并决定如何为一个学习方法编码它们，这对学习方法提取一个好的模型可以产生巨大的影响。建立一个分类器的很多有趣的工作之一是找出哪些特征可能是相关的，以及我们如何能够表示它们。虽然使用相当简单而明显的特征集往往可以得到像样的性能，但是使用精心构建的基于对当前任务的透彻理解的特征，通常会显著提高收益。\n",
    "\n",
    "典型地，特征提取通过反复试验和错误的过程建立的，由哪些信息是与问题相关的直觉指引的。它通常以“厨房水槽”的方法开始，包括你能想到的所有特征，然后检查哪些特征是实际有用的。我们在[1.2](https://usyiyi.github.io/nlp-py-2e-zh/6.html#code-gender-features-overfitting)中对名字性别特征采取这种做法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "057a3213",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gender_features2(name):\n",
    "    features = {}\n",
    "    features[\"first_letter\"] = name[0].lower()\n",
    "    features[\"last_letter\"] = name[-1].lower()\n",
    "    for letter in 'abcdefghijklmnopqrstuvwxyz':\n",
    "        features[\"count({})\".format(letter)] = name.lower().count(letter)\n",
    "        features[\"has({})\".format(letter)] = (letter in name.lower())\n",
    "    return features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf89c784",
   "metadata": {},
   "source": [
    "然而，你要用于一个给定的学习算法的特征的数目是有限的——如果你提供太多的特征，那么该算法将高度依赖你的训练数据的特性，而一般化到新的例子的效果不会很好。这个问题被称为过拟合，当运作在小训练集上时尤其会有问题。例如，如果我们使用[1.2](https://usyiyi.github.io/nlp-py-2e-zh/6.html#code-gender-features-overfitting)中所示的特征提取器训练朴素贝叶斯分类器，将会过拟合这个相对较小的训练集，造成这个系统的精度比只考虑每个名字最后一个字母的分类器的精度低约1％："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6d070cb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.79\n"
     ]
    }
   ],
   "source": [
    "featuresets = [(gender_features2(n), gender) for (n, gender) in labeled_names]\n",
    "train_set, test_set = featuresets[500:], featuresets[:500]\n",
    "classifier = nltk.NaiveBayesClassifier.train(train_set)\n",
    "print(nltk.classify.accuracy(classifier, test_set))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ecb969",
   "metadata": {},
   "source": [
    "一旦初始特征集被选定，完善特征集的一个非常有成效的方法是错误分析。首先，我们选择一个开发集，包含用于创建模型的语料数据。然后将这种开发集分为训练集和开发测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7d2e810d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_names = labeled_names[1500:]\n",
    "devtest_names = labeled_names[500:1500]\n",
    "test_names = labeled_names[:500]"
   ]
  },
  {
   "attachments": {
    "86cecc4bd139ddaf1a5daf9991f39945.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "89a8e10e",
   "metadata": {},
   "source": [
    "训练集用于训练模型，开发测试集用于进行错误分析。测试集用于系统的最终评估。由于下面讨论的原因，我们将一个单独的开发测试集用于错误分析而不是使用测试集是很重要的。在[1.3](https://usyiyi.github.io/nlp-py-2e-zh/6.html#fig-corpus-org)中显示了将语料数据划分成不同的子集。\n",
    "\n",
    "![86cecc4bd139ddaf1a5daf9991f39945.png](attachment:86cecc4bd139ddaf1a5daf9991f39945.png)\n",
    "\n",
    "图 1.3：用于训练有监督分类器的语料数据组织图。语料数据分为两类：开发集和测试集。开发集通常被进一步分为训练集和开发测试集。\n",
    "\n",
    "已经将语料分为适当的数据集，我们使用训练集训练一个模型 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/6.html#err-analysis-train)，然后在开发测试集上运行 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/6.html#err-analysis-run)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9b5214e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.749\n"
     ]
    }
   ],
   "source": [
    "train_set = [(gender_features(n), gender) for (n, gender) in train_names]\n",
    "devtest_set = [(gender_features(n), gender) for (n, gender) in devtest_names]\n",
    "test_set = [(gender_features(n), gender) for (n, gender) in test_names]\n",
    "classifier = nltk.NaiveBayesClassifier.train(train_set) \n",
    "print(nltk.classify.accuracy(classifier, devtest_set)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cacb33ba",
   "metadata": {},
   "source": [
    "使用开发测试集，我们可以生成一个分类器预测名字性别时的错误列表："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "22cf881a",
   "metadata": {},
   "outputs": [],
   "source": [
    "errors = []\n",
    "for (name, tag) in devtest_names:\n",
    "    guess = classifier.classify(gender_features(name))\n",
    "    if guess != tag:\n",
    "        errors.append( (tag, guess, name) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40cda87c",
   "metadata": {},
   "source": [
    "然后，可以检查个别错误案例，在那里该模型预测了错误的标签，尝试确定什么额外信息将使其能够作出正确的决定（或者现有的哪部分信息导致其做出错误的决定）。然后可以相应的调整特征集。我们已经建立的名字分类器在开发测试语料上产生约100个错误："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0faee2d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correct=female   guess=male     name=Abagail                       \n",
      "correct=female   guess=male     name=April                         \n",
      "correct=female   guess=male     name=Ardelis                       \n",
      "correct=female   guess=male     name=Arleen                        \n",
      "correct=female   guess=male     name=Babs                          \n",
      "correct=female   guess=male     name=Bette-Ann                     \n",
      "correct=female   guess=male     name=Brigid                        \n",
      "correct=female   guess=male     name=Calypso                       \n",
      "correct=female   guess=male     name=Carlen                        \n",
      "correct=female   guess=male     name=Caro                          \n",
      "correct=female   guess=male     name=Carolan                       \n",
      "correct=female   guess=male     name=Cathryn                       \n",
      "correct=female   guess=male     name=Cecil                         \n",
      "correct=female   guess=male     name=Charleen                      \n",
      "correct=female   guess=male     name=Charlott                      \n",
      "correct=female   guess=male     name=Charmian                      \n",
      "correct=female   guess=male     name=Cherilynn                     \n",
      "correct=female   guess=male     name=Christabel                    \n",
      "correct=female   guess=male     name=Corliss                       \n",
      "correct=female   guess=male     name=Cybill                        \n",
      "correct=female   guess=male     name=Damaris                       \n",
      "correct=female   guess=male     name=Danit                         \n",
      "correct=female   guess=male     name=Darell                        \n",
      "correct=female   guess=male     name=Darleen                       \n",
      "correct=female   guess=male     name=Daveen                        \n",
      "correct=female   guess=male     name=Dot                           \n",
      "correct=female   guess=male     name=Ealasaid                      \n",
      "correct=female   guess=male     name=Erin                          \n",
      "correct=female   guess=male     name=Eryn                          \n",
      "correct=female   guess=male     name=Evelyn                        \n",
      "correct=female   guess=male     name=Fan                           \n",
      "correct=female   guess=male     name=Felicdad                      \n",
      "correct=female   guess=male     name=Fern                          \n",
      "correct=female   guess=male     name=Floris                        \n",
      "correct=female   guess=male     name=Francis                       \n",
      "correct=female   guess=male     name=Gail                          \n",
      "correct=female   guess=male     name=Gillan                        \n",
      "correct=female   guess=male     name=Gladys                        \n",
      "correct=female   guess=male     name=Glen                          \n",
      "correct=female   guess=male     name=Glyn                          \n",
      "correct=female   guess=male     name=Gretel                        \n",
      "correct=female   guess=male     name=Guendolen                     \n",
      "correct=female   guess=male     name=Gwendolen                     \n",
      "correct=female   guess=male     name=Hester                        \n",
      "correct=female   guess=male     name=Imojean                       \n",
      "correct=female   guess=male     name=Ines                          \n",
      "correct=female   guess=male     name=Jan                           \n",
      "correct=female   guess=male     name=Jess                          \n",
      "correct=female   guess=male     name=Jo-Ann                        \n",
      "correct=female   guess=male     name=Joelynn                       \n",
      "correct=female   guess=male     name=Josselyn                      \n",
      "correct=female   guess=male     name=Kaitlyn                       \n",
      "correct=female   guess=male     name=Karalynn                      \n",
      "correct=female   guess=male     name=Karil                         \n",
      "correct=female   guess=male     name=Karyl                         \n",
      "correct=female   guess=male     name=Kerrill                       \n",
      "correct=female   guess=male     name=Kim                           \n",
      "correct=female   guess=male     name=Kirstin                       \n",
      "correct=female   guess=male     name=Kit                           \n",
      "correct=female   guess=male     name=Koo                           \n",
      "correct=female   guess=male     name=Koral                         \n",
      "correct=female   guess=male     name=Kris                          \n",
      "correct=female   guess=male     name=Kristan                       \n",
      "correct=female   guess=male     name=LeeAnn                        \n",
      "correct=female   guess=male     name=Lib                           \n",
      "correct=female   guess=male     name=Lillis                        \n",
      "correct=female   guess=male     name=Lorrin                        \n",
      "correct=female   guess=male     name=Mairead                       \n",
      "correct=female   guess=male     name=Maren                         \n",
      "correct=female   guess=male     name=Margaret                      \n",
      "correct=female   guess=male     name=Margit                        \n",
      "correct=female   guess=male     name=Mariam                        \n",
      "correct=female   guess=male     name=Maribel                       \n",
      "correct=female   guess=male     name=Marleen                       \n",
      "correct=female   guess=male     name=Maud                          \n",
      "correct=female   guess=male     name=Meghan                        \n",
      "correct=female   guess=male     name=Mel                           \n",
      "correct=female   guess=male     name=Melicent                      \n",
      "correct=female   guess=male     name=Mellisent                     \n",
      "correct=female   guess=male     name=Miriam                        \n",
      "correct=female   guess=male     name=Moreen                        \n",
      "correct=female   guess=male     name=Muriel                        \n",
      "correct=female   guess=male     name=Nadean                        \n",
      "correct=female   guess=male     name=Nat                           \n",
      "correct=female   guess=male     name=Nert                          \n",
      "correct=female   guess=male     name=Nichol                        \n",
      "correct=female   guess=male     name=Perl                          \n",
      "correct=female   guess=male     name=Phillis                       \n",
      "correct=female   guess=male     name=Phyllis                       \n",
      "correct=female   guess=male     name=Phyllys                       \n",
      "correct=female   guess=male     name=Pris                          \n",
      "correct=female   guess=male     name=Raf                           \n",
      "correct=female   guess=male     name=Renel                         \n",
      "correct=female   guess=male     name=Rianon                        \n",
      "correct=female   guess=male     name=Robbyn                        \n",
      "correct=female   guess=male     name=Ruthann                       \n",
      "correct=female   guess=male     name=Sal                           \n",
      "correct=female   guess=male     name=Sherill                       \n",
      "correct=female   guess=male     name=Shir                          \n",
      "correct=female   guess=male     name=Sileas                        \n",
      "correct=female   guess=male     name=Siobhan                       \n",
      "correct=female   guess=male     name=Starlin                       \n",
      "correct=female   guess=male     name=Suzann                        \n",
      "correct=female   guess=male     name=Teriann                       \n",
      "correct=female   guess=male     name=Terri-Jo                      \n",
      "correct=female   guess=male     name=Tim                           \n",
      "correct=female   guess=male     name=Venus                         \n",
      "correct=female   guess=male     name=Winnifred                     \n",
      "correct=male     guess=female   name=Ajai                          \n",
      "correct=male     guess=female   name=Alix                          \n",
      "correct=male     guess=female   name=Alphonse                      \n",
      "correct=male     guess=female   name=Andrea                        \n",
      "correct=male     guess=female   name=Andy                          \n",
      "correct=male     guess=female   name=Angie                         \n",
      "correct=male     guess=female   name=Arnie                         \n",
      "correct=male     guess=female   name=Arvie                         \n",
      "correct=male     guess=female   name=Avery                         \n",
      "correct=male     guess=female   name=Barry                         \n",
      "correct=male     guess=female   name=Bartholemy                    \n",
      "correct=male     guess=female   name=Barty                         \n",
      "correct=male     guess=female   name=Bela                          \n",
      "correct=male     guess=female   name=Benjie                        \n",
      "correct=male     guess=female   name=Blayne                        \n",
      "correct=male     guess=female   name=Broddie                       \n",
      "correct=male     guess=female   name=Broddy                        \n",
      "correct=male     guess=female   name=Brody                         \n",
      "correct=male     guess=female   name=Buddy                         \n",
      "correct=male     guess=female   name=Burnaby                       \n",
      "correct=male     guess=female   name=Chaddy                        \n",
      "correct=male     guess=female   name=Charley                       \n",
      "correct=male     guess=female   name=Chevy                         \n",
      "correct=male     guess=female   name=Christoph                     \n",
      "correct=male     guess=female   name=Clay                          \n",
      "correct=male     guess=female   name=Claybourne                    \n",
      "correct=male     guess=female   name=Clemmie                       \n",
      "correct=male     guess=female   name=Darth                         \n",
      "correct=male     guess=female   name=Demetre                       \n",
      "correct=male     guess=female   name=Doyle                         \n",
      "correct=male     guess=female   name=Dwaine                        \n",
      "correct=male     guess=female   name=Dwane                         \n",
      "correct=male     guess=female   name=Eddie                         \n",
      "correct=male     guess=female   name=Ellsworth                     \n",
      "correct=male     guess=female   name=Emmery                        \n",
      "correct=male     guess=female   name=Emory                         \n",
      "correct=male     guess=female   name=Felice                        \n",
      "correct=male     guess=female   name=Filmore                       \n",
      "correct=male     guess=female   name=Fox                           \n",
      "correct=male     guess=female   name=Gary                          \n",
      "correct=male     guess=female   name=Gayle                         \n",
      "correct=male     guess=female   name=George                        \n",
      "correct=male     guess=female   name=Gerri                         \n",
      "correct=male     guess=female   name=Giffie                        \n",
      "correct=male     guess=female   name=Giuseppe                      \n",
      "correct=male     guess=female   name=Godfree                       \n",
      "correct=male     guess=female   name=Hailey                        \n",
      "correct=male     guess=female   name=Harvey                        \n",
      "correct=male     guess=female   name=Harvie                        \n",
      "correct=male     guess=female   name=Hersch                        \n",
      "correct=male     guess=female   name=Hersh                         \n",
      "correct=male     guess=female   name=Hilary                        \n",
      "correct=male     guess=female   name=Hodge                         \n",
      "correct=male     guess=female   name=Iggy                          \n",
      "correct=male     guess=female   name=Ike                           \n",
      "correct=male     guess=female   name=Isa                           \n",
      "correct=male     guess=female   name=Jae                           \n",
      "correct=male     guess=female   name=Jamie                         \n",
      "correct=male     guess=female   name=Jay                           \n",
      "correct=male     guess=female   name=Jean-Pierre                   \n",
      "correct=male     guess=female   name=Jedediah                      \n",
      "correct=male     guess=female   name=Jeffery                       \n",
      "correct=male     guess=female   name=Jeffie                        \n",
      "correct=male     guess=female   name=Jefry                         \n",
      "correct=male     guess=female   name=Jephthah                      \n",
      "correct=male     guess=female   name=Jeremiah                      \n",
      "correct=male     guess=female   name=Jerzy                         \n",
      "correct=male     guess=female   name=Jimmy                         \n",
      "correct=male     guess=female   name=Kennedy                       \n",
      "correct=male     guess=female   name=Kory                          \n",
      "correct=male     guess=female   name=Lindsey                       \n",
      "correct=male     guess=female   name=Lonnie                        \n",
      "correct=male     guess=female   name=Martie                        \n",
      "correct=male     guess=female   name=Marve                         \n",
      "correct=male     guess=female   name=Matty                         \n",
      "correct=male     guess=female   name=Maurie                        \n",
      "correct=male     guess=female   name=Maxie                         \n",
      "correct=male     guess=female   name=Meredeth                      \n",
      "correct=male     guess=female   name=Monte                         \n",
      "correct=male     guess=female   name=Morry                         \n",
      "correct=male     guess=female   name=Munroe                        \n",
      "correct=male     guess=female   name=Nickey                        \n",
      "correct=male     guess=female   name=Obie                          \n",
      "correct=male     guess=female   name=Odie                          \n",
      "correct=male     guess=female   name=Paddy                         \n",
      "correct=male     guess=female   name=Paige                         \n",
      "correct=male     guess=female   name=Pascale                       \n",
      "correct=male     guess=female   name=Pasquale                      \n",
      "correct=male     guess=female   name=Pattie                        \n",
      "correct=male     guess=female   name=Perry                         \n",
      "correct=male     guess=female   name=Pete                          \n",
      "correct=male     guess=female   name=Quiggly                       \n",
      "correct=male     guess=female   name=Quincy                        \n",
      "correct=male     guess=female   name=Ramsey                        \n",
      "correct=male     guess=female   name=Randi                         \n",
      "correct=male     guess=female   name=Reggy                         \n",
      "correct=male     guess=female   name=Rene                          \n",
      "correct=male     guess=female   name=Ricky                         \n",
      "correct=male     guess=female   name=Rory                          \n",
      "correct=male     guess=female   name=Roth                          \n",
      "correct=male     guess=female   name=Rudy                          \n",
      "correct=male     guess=female   name=Rutledge                      \n",
      "correct=male     guess=female   name=Sawyere                       \n",
      "correct=male     guess=female   name=Say                           \n",
      "correct=male     guess=female   name=Selby                         \n",
      "correct=male     guess=female   name=Serge                         \n",
      "correct=male     guess=female   name=Sergei                        \n",
      "correct=male     guess=female   name=Shayne                        \n",
      "correct=male     guess=female   name=Sheffie                       \n",
      "correct=male     guess=female   name=Shelley                       \n",
      "correct=male     guess=female   name=Sherlocke                     \n",
      "correct=male     guess=female   name=Sidnee                        \n",
      "correct=male     guess=female   name=Sidney                        \n",
      "correct=male     guess=female   name=Sinclare                      \n",
      "correct=male     guess=female   name=Stanleigh                     \n",
      "correct=male     guess=female   name=Stearne                       \n",
      "correct=male     guess=female   name=Stinky                        \n",
      "correct=male     guess=female   name=Tannie                        \n",
      "correct=male     guess=female   name=Thaine                        \n",
      "correct=male     guess=female   name=Thayne                        \n",
      "correct=male     guess=female   name=Tome                          \n",
      "correct=male     guess=female   name=Torrey                        \n",
      "correct=male     guess=female   name=Tulley                        \n",
      "correct=male     guess=female   name=Tymothy                       \n",
      "correct=male     guess=female   name=Uli                           \n",
      "correct=male     guess=female   name=Uri                           \n",
      "correct=male     guess=female   name=Vance                         \n",
      "correct=male     guess=female   name=Verne                         \n",
      "correct=male     guess=female   name=Wadsworth                     \n",
      "correct=male     guess=female   name=Waine                         \n",
      "correct=male     guess=female   name=Waite                         \n",
      "correct=male     guess=female   name=Wake                          \n",
      "correct=male     guess=female   name=Wallie                        \n",
      "correct=male     guess=female   name=Wash                          \n",
      "correct=male     guess=female   name=Way                           \n",
      "correct=male     guess=female   name=Welbie                        \n",
      "correct=male     guess=female   name=Witty                         \n",
      "correct=male     guess=female   name=Wojciech                      \n",
      "correct=male     guess=female   name=Wye                           \n",
      "correct=male     guess=female   name=Yale                          \n",
      "correct=male     guess=female   name=Yancy                         \n",
      "correct=male     guess=female   name=Zacherie                      \n",
      "correct=male     guess=female   name=Zachery                       \n"
     ]
    }
   ],
   "source": [
    "for (tag, guess, name) in sorted(errors):\n",
    "    print('correct={:<8} guess={:<8s} name={:<30}'.format(tag, guess, name))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc0403e6",
   "metadata": {},
   "source": [
    "浏览这个错误列表，它明确指出一些多个字母的后缀可以指示名字性别。例如，yn结尾的名字显示以女性为主，尽管事实上，n结尾的名字往往是男性；以ch结尾的名字通常是男性，尽管以h结尾的名字倾向于是女性。因此，调整我们的特征提取器包括两个字母后缀的特征："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f980d1bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gender_features(word):\n",
    "    return {'suffix1': word[-1:],\n",
    "            'suffix2': word[-2:]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1ad7f54",
   "metadata": {},
   "source": [
    "使用新的特征提取器重建分类器，我们看到测试数据集上的性能提高了近3个百分点（从76.5％到78.2％）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "30834d0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.768\n"
     ]
    }
   ],
   "source": [
    "train_set = [(gender_features(n), gender) for (n, gender) in train_names]\n",
    "devtest_set = [(gender_features(n), gender) for (n, gender) in devtest_names]\n",
    "classifier = nltk.NaiveBayesClassifier.train(train_set)\n",
    "print(nltk.classify.accuracy(classifier, devtest_set))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8bd3def",
   "metadata": {},
   "source": [
    "这个错误分析过程可以不断重复，检查存在于由新改进的分类器产生的错误中的模式。每一次错误分析过程被重复，我们应该选择一个不同的开发测试/训练分割，以确保该分类器不会开始反映开发测试集的特质。\n",
    "\n",
    "但是，一旦我们已经使用了开发测试集帮助我们开发模型，关于这个模型在新数据会表现多好，我们将不能再相信它会给我们一个准确地结果。因此，保持测试集分离、未使用过，直到我们的模型开发完毕是很重要的。在这一点上，我们可以使用测试集评估模型在新的输入值上执行的有多好。\n",
    "\n",
    "## 1.3 文档分类\n",
    "\n",
    "在 [1](https://usyiyi.github.io/nlp-py-2e-zh/2.html#sec-extracting-text-from-corpora)中，我们看到了语料库的几个例子，那里文档已经按类别标记。使用这些语料库，我们可以建立分类器，自动给新文档添加适当的类别标签。首先，我们构造一个标记了相应类别的文档清单。对于这个例子，我们选择电影评论语料库，将每个评论归类为正面或负面。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cfbdbb06",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import movie_reviews\n",
    "documents = [(list(movie_reviews.words(fileid)), category)\n",
    "             for category in movie_reviews.categories()\n",
    "             for fileid in movie_reviews.fileids(category)]\n",
    "random.shuffle(documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "659503f4",
   "metadata": {},
   "source": [
    "接下来，我们为文档定义一个特征提取器，这样分类器就会知道哪些方面的数据应注意（[1.4](https://usyiyi.github.io/nlp-py-2e-zh/6.html#code-document-classify-fd)）。对于文档主题识别，我们可以为每个词定义一个特性表示该文档是否包含这个词。为了限制分类器需要处理的特征的数目，我们一开始构建一个整个语料库中前2000个最频繁词的列表 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/6.html#document-classify-all-words)。然后，定义一个特征提取器 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/6.html#document-classify-extractor)，简单地检查这些词是否在一个给定的文档中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "97ae4319",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words())\n",
    "word_features = list(all_words)[:2000] \n",
    "\n",
    "def document_features(document): \n",
    "    document_words = set(document) \n",
    "    features = {}\n",
    "    for word in word_features:\n",
    "        features['contains({})'.format(word)] = (word in document_words)\n",
    "    return features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceb1b5ea",
   "metadata": {},
   "source": [
    "注意\n",
    "\n",
    "在 [# 3](https://usyiyi.github.io/nlp-py-2e-zh/6.html#document-classify-set)中我们计算文档的所有词的集合，而不仅仅检查是否`word in document`，因为检查一个词是否在一个集合中出现比检查它是否在一个列表中出现要快的多（[4.7](https://usyiyi.github.io/nlp-py-2e-zh/4.html#sec-algorithm-design)）。\n",
    "\n",
    "现在，我们已经定义了我们的特征提取器，可以用它来训练一个分类器，为新的电影评论加标签（[1.5](https://usyiyi.github.io/nlp-py-2e-zh/6.html#code-document-classify-use)）。为了检查产生的分类器可靠性如何，我们在测试集上计算其准确性 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/6.html#document-classify-test)。再一次的，我们可以使用`show_most_informative_features()`来找出哪些特征是分类器发现最有信息量的 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/6.html#document-classify-smif)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8399d994",
   "metadata": {},
   "outputs": [],
   "source": [
    "featuresets = [(document_features(d), c) for (d,c) in documents]\n",
    "train_set, test_set = featuresets[100:], featuresets[:100]\n",
    "classifier = nltk.NaiveBayesClassifier.train(train_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "256b7ff6",
   "metadata": {},
   "source": [
    "显然在这个语料库中，提到Seagal的评论中负面的是正面的大约8倍，而提到Damon的评论中正面的是负面的大约6倍。\n",
    "\n",
    "## 1.4 词性标注\n",
    "\n",
    "第[5.](https://usyiyi.github.io/nlp-py-2e-zh/5.html#chap-tag)中，我们建立了一个正则表达式标注器，通过查找词内部的组成，为词选择词性标记。然而，这个正则表达式标注器是手工制作的。作为替代，我们可以训练一个分类器来算出哪个后缀最有信息量。首先，让我们找出最常见的后缀："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "21286099",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['e', ',', '.', 's', 'd', 't', 'he', 'n', 'a', 'of', 'the', 'y', 'r', 'to', 'in', 'f', 'o', 'ed', 'nd', 'is', 'on', 'l', 'g', 'and', 'ng', 'er', 'as', 'ing', 'h', 'at', 'es', 'or', 're', 'it', '``', 'an', \"''\", 'm', ';', 'i', 'ly', 'ion', 'en', 'al', '?', 'nt', 'be', 'hat', 'st', 'his', 'th', 'll', 'le', 'ce', 'by', 'ts', 'me', 've', \"'\", 'se', 'ut', 'was', 'for', 'ent', 'ch', 'k', 'w', 'ld', '`', 'rs', 'ted', 'ere', 'her', 'ne', 'ns', 'ith', 'ad', 'ry', ')', '(', 'te', '--', 'ay', 'ty', 'ot', 'p', 'nce', \"'s\", 'ter', 'om', 'ss', ':', 'we', 'are', 'c', 'ers', 'uld', 'had', 'so', 'ey']\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import brown\n",
    "suffix_fdist = nltk.FreqDist()\n",
    "for word in brown.words():\n",
    "    word = word.lower()\n",
    "    suffix_fdist[word[-1:]] += 1\n",
    "    suffix_fdist[word[-2:]] += 1\n",
    "    suffix_fdist[word[-3:]] += 1\n",
    "common_suffixes = [suffix for (suffix, count) in suffix_fdist.most_common(100)]\n",
    "print(common_suffixes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f838be3c",
   "metadata": {},
   "source": [
    "接下来，我们将定义一个特征提取器函数，检查给定的单词的这些后缀："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "507b7915",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pos_features(word):\n",
    "    features = {}\n",
    "    for suffix in common_suffixes:\n",
    "        features['endswith({})'.format(suffix)] = word.lower().endswith(suffix)\n",
    "    return features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "866284e8",
   "metadata": {},
   "source": [
    "特征提取函数的行为就像有色眼镜一样，强调我们的数据中的某些属性（颜色），并使其无法看到其他属性。分类器在决定如何标记输入时，将完全依赖它们强调的属性。在这种情况下，分类器将只基于一个给定的词拥有（如果有）哪个常见后缀的信息来做决定。\n",
    "\n",
    "现在，我们已经定义了我们的特征提取器，可以用它来训练一个新的“决策树”的分类器（将在[4](https://usyiyi.github.io/nlp-py-2e-zh/6.html#sec-decision-trees)讨论）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3de4e50b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tagged_words = brown.tagged_words(categories='news')\n",
    "featuresets = [(pos_features(n), g) for (n,g) in tagged_words]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "13aab604",
   "metadata": {},
   "outputs": [],
   "source": [
    "size = int(len(featuresets) * 0.1)\n",
    "train_set, test_set = featuresets[size:], featuresets[:size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9ca4800a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6270512182993535"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = nltk.DecisionTreeClassifier.train(train_set)\n",
    "nltk.classify.accuracy(classifier, test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "16a0792a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NNS'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.classify(pos_features('cats'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d63b53ba",
   "metadata": {},
   "source": [
    "决策树模型的一个很好的性质是它们往往很容易解释——我们甚至可以指示NLTK将它们以伪代码形式输出："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "82c4242d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "if endswith(the) == False: \n",
      "  if endswith(,) == False: \n",
      "    if endswith(s) == False: \n",
      "      if endswith(.) == False: return '.'\n",
      "      if endswith(.) == True: return '.'\n",
      "    if endswith(s) == True: \n",
      "      if endswith(is) == False: return 'PP$'\n",
      "      if endswith(is) == True: return 'BEZ'\n",
      "  if endswith(,) == True: return ','\n",
      "if endswith(the) == True: return 'AT'\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classifier.pseudocode(depth=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79967f15",
   "metadata": {},
   "source": [
    "在这里，我们可以看到分类器一开始检查一个词是否以逗号结尾——如果是，它会得到一个特别的标记`\",\"`。接下来，分类器检查词是否以`\"the\"`尾，这种情况它几乎肯定是一个限定词。这个“后缀”被决策树早早使用是因为词\"the\"太常见。分类器继续检查词是否以\"s\"结尾。如果是，那么它极有可能得到动词标记`VBZ`（除非它是这个词\"is\"，它有特殊标记`BEZ`），如果不是，那么它往往是名词（除非它是标点符号“.”）。实际的分类器包含这里显示的if-then语句下面进一步的嵌套，参数`depth=4` 只显示决策树的顶端部分。\n",
    "\n",
    "## 1.5 探索上下文语境\n",
    "\n",
    "通过增加特征提取函数，我们可以修改这个词性标注器来利用各种词内部的其他特征，例如词长、它所包含的音节数或者它的前缀。然而，只要特征提取器仅仅看着目标词，我们就没法添加依赖词出现的*上下文语境*特征。然而上下文语境特征往往提供关于正确标记的强大线索——例如，标注词\"fly\"，如果知道它前面的词是“a”将使我们能够确定它是一个名词，而不是一个动词。\n",
    "\n",
    "为了采取基于词的上下文的特征，我们必须修改以前为我们的特征提取器定义的模式。不是只传递已标注的词，我们将传递整个（未标注的）句子，以及目标词的索引。[1.6](https://usyiyi.github.io/nlp-py-2e-zh/6.html#code-suffix-pos-tag)演示了这种方法，使用依赖上下文的特征提取器定义一个词性标记分类器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2ee32093",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pos_features(sentence, i): \n",
    "    features = {\"suffix(1)\": sentence[i][-1:],\n",
    "                \"suffix(2)\": sentence[i][-2:],\n",
    "                \"suffix(3)\": sentence[i][-3:]}\n",
    "    if i == 0:\n",
    "        features[\"prev-word\"] = \"<START>\"\n",
    "    else:\n",
    "        features[\"prev-word\"] = sentence[i-1]\n",
    "    return features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d7814d5",
   "metadata": {},
   "source": [
    "很显然，利用上下文特征提高了我们的词性标注器的准确性。例如，分类器学到一个词如果紧跟在词\"large\"或\"gubernatorial\"后面，极可能是名词。然而，它无法学到，一个词如果它跟在形容词后面可能是名词，这样更一般的，因为它没有获得前面这个词的词性标记。在一般情况下，简单的分类器总是将每一个输入与所有其他输入独立对待。在许多情况下，这非常有道理。例如，关于名字是否倾向于男性或女性的决定可以通过具体分析来做出。然而，有很多情况，如词性标注，我们感兴趣的是解决彼此密切相关的分类问题。\n",
    "\n",
    "## 1.6 序列分类\n",
    "\n",
    "为了捕捉相关的分类任务之间的依赖关系，我们可以使用联合分类器模型，收集有关输入，选择适当的标签。在词性标注的例子中，各种不同的序列分类器模型可以被用来为一个给定的句子中的所有的词共同选择词性标签。\n",
    "\n",
    "一种序列分类器策略，称为连续分类或贪婪序列分类，是为第一个输入找到最有可能的类标签，然后使用这个问题的答案帮助找到下一个输入的最佳的标签。这个过程可以不断重复直到所有的输入都被贴上标签。这是[5](https://usyiyi.github.io/nlp-py-2e-zh/5.html#sec-n-gram-tagging)中的二元标注器采用的方法，它一开始为句子的第一个词选择词性标记，然后为每个随后的词选择标记，基于词本身和前面词的预测的标记。\n",
    "\n",
    "在[1.7](https://usyiyi.github.io/nlp-py-2e-zh/6.html#code-consecutive-pos-tagger)中演示了这一策略。首先，我们必须扩展我们的特征提取函数使其具有参数`history`，它提供一个我们到目前为止已经为句子预测的标记的列表 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/6.html#consec-pos-tag-features)。`history`中的每个标记对应`sentence`中的一个词。但是请注意，`history`将只包含我们已经归类的词的标记，也就是目标词左侧的词。因此，虽然是有可能查看目标词右边的词的某些特征，但查看那些词的标记是不可能的（因为我们还未产生它们）。\n",
    "\n",
    "已经定义了特征提取器，我们可以继续建立我们的序列分类器 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/6.html#consec-pos-tagger)。在训练中，我们使用已标注的标记为征提取器提供适当的历史信息，但标注新的句子时，我们基于标注器本身的输出产生历史信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "970dc3e8",
   "metadata": {},
   "outputs": [],
   "source": [
    " def pos_features(sentence, i, history): \n",
    "     features = {\"suffix(1)\": sentence[i][-1:],\n",
    "                 \"suffix(2)\": sentence[i][-2:],\n",
    "                 \"suffix(3)\": sentence[i][-3:]}\n",
    "     if i == 0:\n",
    "         features[\"prev-word\"] = \"<START>\"\n",
    "         features[\"prev-tag\"] = \"<START>\"\n",
    "     else:\n",
    "         features[\"prev-word\"] = sentence[i-1]\n",
    "         features[\"prev-tag\"] = history[i-1]\n",
    "     return features\n",
    "\n",
    "class ConsecutivePosTagger(nltk.TaggerI): \n",
    "\n",
    "    def __init__(self, train_sents):\n",
    "        train_set = []\n",
    "        for tagged_sent in train_sents:\n",
    "            untagged_sent = nltk.tag.untag(tagged_sent)\n",
    "            history = []\n",
    "            for i, (word, tag) in enumerate(tagged_sent):\n",
    "                featureset = pos_features(untagged_sent, i, history)\n",
    "                train_set.append( (featureset, tag) )\n",
    "                history.append(tag)\n",
    "        self.classifier = nltk.NaiveBayesClassifier.train(train_set)\n",
    "\n",
    "    def tag(self, sentence):\n",
    "        history = []\n",
    "        for i, word in enumerate(sentence):\n",
    "            featureset = pos_features(sentence, i, history)\n",
    "            tag = self.classifier.classify(featureset)\n",
    "            history.append(tag)\n",
    "        return zip(sentence, history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e05feba",
   "metadata": {},
   "source": [
    "## 1.7 其他序列分类方法\n",
    "\n",
    "这种方法的一个缺点是我们的决定一旦做出无法更改。例如，如果我们决定将一个词标注为名词，但后来发现的证据表明应该是一个动词，就没有办法回去修复我们的错误。这个问题的一个解决方案是采取转型策略。转型联合分类的工作原理是为输入的标签创建一个初始值，然后反复提炼那个值，尝试修复相关输入之间的不一致。Brill标注器，[(1)](https://usyiyi.github.io/nlp-py-2e-zh/5.html#sec-transformation-based-tagging)中描述的，是这种策略的一个很好的例子。\n",
    "\n",
    "另一种方案是为词性标记所有可能的序列打分，选择总得分最高的序列。隐马尔可夫模型就采取这种方法。隐马尔可夫模型类似于连续分类器，它不光看输入也看已预测标记的历史。然而，不是简单地找出一个给定的词的单个最好的标签，而是为标记产生一个概率分布。然后将这些概率结合起来计算标记序列的概率得分，最高概率的标记序列会被选中。不幸的是，可能的标签序列的数量相当大。给定30 个标签的标记集，有大约600 万亿（3010）种方式来标记一个10个词的句子。为了避免单独考虑所有这些可能的序列，隐马尔可夫模型要求特征提取器只看最近的标记（或最近的n个标记，其中n是相当小的）。由于这种限制，它可以使用动态规划（[4.7](https://usyiyi.github.io/nlp-py-2e-zh/4.html#sec-algorithm-design)），有效地找出最有可能的标记序列。特别是，对每个连续的词索引i，每个可能的当前及以前的标记都被计算得分。这种同样基础的方法被两个更先进的模型所采用，它们被称为最大熵马尔可夫模型和线性链条件随机场模型；但为标记序列打分用的是不同的算法。\n",
    "\n",
    "## 2 有监督分类的更多例子\n",
    "\n",
    "**vscode jupyter TOC**\n",
    "\n",
    "<a href=\"#21-句子分割\">2.1 句子分割</a> \n",
    "\n",
    "<a href=\"#22-识别对话行为类型\">2.2 识别对话行为类型</a> \n",
    "\n",
    "<a href=\"#23-识别文字蕴含\">2.3 识别文字蕴含</a> \n",
    "\n",
    "<a href=\"#24-扩展到大型数据集\">2.4 扩展到大型数据集</a> \n",
    "\n",
    "**jupyter notebook TOC**\n",
    "\n",
    "<a href=\"#2.1-句子分割\">2.1 句子分割</a> \n",
    "\n",
    "<a href=\"#2.2-识别对话行为类型\">2.2 识别对话行为类型</a> \n",
    "\n",
    "<a href=\"#2.3-识别文字蕴含\">2.3 识别文字蕴含</a> \n",
    "\n",
    "<a href=\"#2.4-扩展到大型数据集\">2.4 扩展到大型数据集</a> \n",
    "\n",
    "## 2.1 句子分割\n",
    "\n",
    "句子分割可以看作是一个标点符号的分类任务：每当我们遇到一个可能会结束一个句子的符号，如句号或问号，我们必须决定它是否终止了当前句子。\n",
    "\n",
    "第一步是获得一些已被分割成句子的数据，将它转换成一种适合提取特征的形式："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dea7864c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sents = nltk.corpus.treebank_raw.sents()\n",
    "tokens = []\n",
    "boundaries = set()\n",
    "offset = 0\n",
    "for sent in sents:\n",
    "    tokens.extend(sent)\n",
    "    offset += len(sent)\n",
    "    boundaries.add(offset-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28d268a8",
   "metadata": {},
   "source": [
    "在这里，`tokens`是单独句子标识符的合并列表，`boundaries`是一个包含所有句子边界词符索引的集合。下一步，我们需要指定用于决定标点是否表示句子边界的数据特征："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "98f86760",
   "metadata": {},
   "outputs": [],
   "source": [
    "def punct_features(tokens, i):\n",
    "    return {'next-word-capitalized': tokens[i+1][0].isupper(),\n",
    "            'prev-word': tokens[i-1].lower(),\n",
    "            'punct': tokens[i],\n",
    "            'prev-word-is-one-char': len(tokens[i-1]) == 1}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9f9103",
   "metadata": {},
   "source": [
    "基于这一特征提取器，我们可以通过选择所有的标点符号创建一个加标签的特征集的列表，然后标注它们是否是边界标识符："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d6690e25",
   "metadata": {},
   "outputs": [],
   "source": [
    "featuresets = [(punct_features(tokens, i), (i in boundaries))\n",
    "               for i in range(1, len(tokens)-1)\n",
    "               if tokens[i] in '.?!']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c036493",
   "metadata": {},
   "source": [
    "使用这些特征集，我们可以训练和评估一个标点符号分类器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a63ea8b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.936026936026936"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size = int(len(featuresets) * 0.1)\n",
    "train_set, test_set = featuresets[size:], featuresets[:size]\n",
    "classifier = nltk.NaiveBayesClassifier.train(train_set)\n",
    "nltk.classify.accuracy(classifier, test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337cffe2",
   "metadata": {},
   "source": [
    "使用这种分类器进行断句，我们只需检查每个标点符号，看它是否是作为一个边界标识符；在边界标识符处分割词列表。在[2.1](https://usyiyi.github.io/nlp-py-2e-zh/6.html#code-classification-based-segmenter)中的清单显示了如何可以做到这一点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "00b2ca95",
   "metadata": {},
   "outputs": [],
   "source": [
    "def segment_sentences(words):\n",
    "    start = 0\n",
    "    sents = []\n",
    "    for i, word in enumerate(words):\n",
    "        if word in '.?!' and classifier.classify(punct_features(words, i)) == True:\n",
    "            sents.append(words[start:i+1])\n",
    "            start = i+1\n",
    "    if start < len(words):\n",
    "        sents.append(words[start:])\n",
    "    return sents"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe3cbe7",
   "metadata": {},
   "source": [
    "## 2.2 识别对话行为类型\n",
    "\n",
    "处理对话时，将对话看作说话者执行的*行为*是很有用的。对于表述行为的陈述句这种解释是最直白的，例如\"I forgive you\"或\"I bet you can't climb that hill\"。但是问候、问题、回答、断言和说明都可以被认为是基于语言的行为类型。识别对话中言语下的对话行为是理解谈话的重要的第一步。\n",
    "\n",
    "NPS聊天语料库，在[1](https://usyiyi.github.io/nlp-py-2e-zh/2.html#sec-extracting-text-from-corpora)中的展示过，包括超过10,000个来自即时消息会话的帖子。这些帖子都已经被贴上15 种对话行为类型中的一种标签，例如“陈述”，“情感”，“yn问题”和“Continuer”。因此，我们可以利用这些数据建立一个分类器，识别新的即时消息帖子的对话行为类型。第一步是提取基本的消息数据。我们将调用`xml_posts()`来得到一个数据结构，表示每个帖子的XML注释："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d88319ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "posts = nltk.corpus.nps_chat.xml_posts()[:10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeddaeb5",
   "metadata": {},
   "source": [
    "下一步，我们将定义一个简单的特征提取器，检查帖子包含什么词："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bd81fb28",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dialogue_act_features(post):\n",
    "    features = {}\n",
    "    for word in nltk.word_tokenize(post):\n",
    "        features['contains({})'.format(word.lower())] = True\n",
    "    return features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a57923e",
   "metadata": {},
   "source": [
    "最后，我们通过为每个帖子提取特征（使用`post.get('class')`获得一个帖子的对话行为类型）构造训练和测试数据，并创建一个新的分类器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3b011c5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.667\n"
     ]
    }
   ],
   "source": [
    "featuresets = [(dialogue_act_features(post.text), post.get('class'))\n",
    "               for post in posts]\n",
    "size = int(len(featuresets) * 0.1)\n",
    "train_set, test_set = featuresets[size:], featuresets[:size]\n",
    "classifier = nltk.NaiveBayesClassifier.train(train_set)\n",
    "print(nltk.classify.accuracy(classifier, test_set))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1236ef19",
   "metadata": {},
   "source": [
    "## 2.3 识别文字蕴含\n",
    "\n",
    "识别文字蕴含（RTE）是判断文本*T*的一个给定片段是否蕴含着另一个叫做“假设”的文本（已经在[5](https://usyiyi.github.io/nlp-py-2e-zh/1.html#sec-automatic-natural-language-understanding)讨论过）。迄今为止，已经有4个RTE挑战赛，在那里共享的开发和测试数据会提供给参赛队伍。这里是挑战赛3开发数据集中的文本/假设对的两个例子。标签*True*表示蕴含成立，*False*表示蕴含不成立。\n",
    "\n",
    "> Challenge 3, Pair 34 (True)\n",
    ">\n",
    "> > **T**: Parviz Davudi was representing Iran at a meeting of the Shanghai Co-operation Organisation (SCO), the fledgling association that binds Russia, China and four former Soviet republics of central Asia together to fight terrorism.\n",
    "> >\n",
    "> > **H**: China is a member of SCO.\n",
    ">\n",
    "> Challenge 3, Pair 81 (False)\n",
    ">\n",
    "> > **T**: According to NC Articles of Organization, the members of LLC company are H. Nelson Beavers, III, H. Chester Beavers and Jennie Beavers Stewart.\n",
    "> >\n",
    "> > **H**: Jennie Beavers Stewart is a share-holder of Carolina Analytical Laboratory.\n",
    "\n",
    "应当强调，文字和假设之间的关系并不一定是逻辑蕴涵，而是一个人是否会得出结论：文本提供了合理的证据证明假设是真实的。\n",
    "\n",
    "我们可以把RTE当作一个分类任务，尝试为每一对预测*True*/*False*标签。虽然这项任务的成功做法似乎看上去涉及语法分析、语义和现实世界的知识的组合，RTE的许多早期的尝试使用粗浅的分析基于文字和假设之间的在词级别的相似性取得了相当不错的结果。在理想情况下，我们希望如果有一个蕴涵那么假设所表示的所有信息也应该在文本中表示。相反，如果假设中有的资料文本中没有，那么就没有蕴涵。\n",
    "\n",
    "在我们的RTE 特征探测器（[2.2](https://usyiyi.github.io/nlp-py-2e-zh/6.html#code-rte-features)）中，我们让词（即词类型）作为信息的代理，我们的特征计数词重叠的程度和假设中有而文本中没有的词的程度（由`hyp_extra()`方法获取）。不是所有的词都是同样重要的——命名实体，如人、组织和地方的名称，可能会更为重要，这促使我们分别为`word`s和`ne`s（命名实体）提取不同的信息。此外，一些高频虚词作为“停用词”被过滤掉。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ce0c9a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rte_features(rtepair):\n",
    "    extractor = nltk.RTEFeatureExtractor(rtepair)\n",
    "    features = {}\n",
    "    features['word_overlap'] = len(extractor.overlap('word'))\n",
    "    features['word_hyp_extra'] = len(extractor.hyp_extra('word'))\n",
    "    features['ne_overlap'] = len(extractor.overlap('ne'))\n",
    "    features['ne_hyp_extra'] = len(extractor.hyp_extra('ne'))\n",
    "    return features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed577340",
   "metadata": {},
   "source": [
    "为了说明这些特征的内容，我们检查前面显示的文本/假设对34的一些属性："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7c0a0d1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Iran', 'Shanghai', 'republics', 'terrorism.', 'binds', 'Asia', 'Co', 'central', 'meeting', 'operation', 'Parviz', 'Soviet', 'that', 'together', 'four', 'fledgling', 'representing', 'SCO', 'was', 'China', 'Davudi', 'Organisation', 'at', 'former', 'association', 'fight', 'Russia'}\n"
     ]
    }
   ],
   "source": [
    "rtepair = nltk.corpus.rte.pairs(['rte3_dev.xml'])[33]\n",
    "extractor = nltk.RTEFeatureExtractor(rtepair)\n",
    "print(extractor.text_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "caa668ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'SCO.', 'China', 'member'}\n"
     ]
    }
   ],
   "source": [
    "print(extractor.hyp_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e67577cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set()\n"
     ]
    }
   ],
   "source": [
    "print(extractor.overlap('word'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c712a807",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'China'}\n"
     ]
    }
   ],
   "source": [
    "print(extractor.overlap('ne'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c29eaf04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'member'}\n"
     ]
    }
   ],
   "source": [
    "print(extractor.hyp_extra('word'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aee4293",
   "metadata": {},
   "source": [
    "这些特征表明假设中所有重要的词都包含在文本中，因此有一些证据支持标记这个为*True*。\n",
    "\n",
    "`nltk.classify.rte_classify`模块使用这些方法在合并的RTE测试数据上取得了刚刚超过58％的准确率。这个数字并不是很令人印象深刻的，还需要大量的努力，更多的语言学处理，才能达到更好的结果。\n",
    "\n",
    "## 2.4 扩展到大型数据集\n",
    "\n",
    "Python提供了一个良好的环境进行基本的文本处理和特征提取。然而，它处理机器学习方法需要的密集数值计算不能够如C语言那样的低级语言那么快。因此，如果你尝试在大型数据集使用纯Python 的机器学习实现（如`nltk.NaiveBayesClassifier`），你可能会发现学习算法会花费大量的时间和内存。\n",
    "\n",
    "如果你打算用大量训练数据或大量特征来训练分类器，我们建议你探索NLTK与外部机器学习包的接口。只要这些软件包已安装，NLTK可以透明地调用它们（通过系统调用）来训练分类模型，明显比纯Python的分类实现快。请看NLTK网站上推荐的NLTK支持的机器学习包列表。\n",
    "\n",
    "## 3 评估\n",
    "\n",
    "为了决定一个分类模型是否准确地捕捉了模式，我们必须评估该模型。评估的结果对于决定模型是多么值得信赖以及我们如何使用它是非常重要。评估也可以是一个有效的工具，用于指导我们在未来改进模型。\n",
    "\n",
    "**vscode jupyter TOC**\n",
    "\n",
    "<a href=\"#31-测试集\">3.1 测试集</a> \n",
    "\n",
    "<a href=\"#32-准确度\">3.2 准确度</a> \n",
    "\n",
    "<a href=\"#33-精确度和召回率\">3.3 精确度和召回率</a> \n",
    "\n",
    "<a href=\"#34-混淆矩阵\">3.4 混淆矩阵</a> \n",
    "\n",
    "<a href=\"#35-交叉验证\">3.5 交叉验证</a> \n",
    "\n",
    "**jupyter notebook TOC**\n",
    "\n",
    "<a href=\"#3.1-测试集\">3.1 测试集</a> \n",
    "\n",
    "<a href=\"#3.2-准确度\">3.2 准确度</a> \n",
    "\n",
    "<a href=\"#3.3-精确度和召回率\">3.3 精确度和召回率</a> \n",
    "\n",
    "<a href=\"#3.4-混淆矩阵\">3.4 混淆矩阵</a> \n",
    "\n",
    "<a href=\"#3.5-交叉验证\">3.5 交叉验证</a> \n",
    "\n",
    "\n",
    "## 3.1 测试集\n",
    "\n",
    "大多数评估技术为模型计算一个得分，通过比较它在测试集（或评估集）中为输入生成的标签与那些输入的正确标签。该测试集通常与训练集具有相同的格式。然而，测试集与训练语料不同是非常重要的：如果我们简单地重复使用训练集作为测试集，那么一个只记住了它的输入而没有学会如何推广到新的例子的模型会得到误导人的高分。\n",
    "\n",
    "建立测试集时，往往是一个可用于测试的和可用于训练的数据量之间的权衡。对于有少量平衡的标签和一个多样化的测试集的分类任务，只要100个评估实例就可以进行有意义的评估。但是，如果一个分类任务有大量的标签或包括非常罕见的标签，那么选择的测试集的大小就要保证出现次数最少的标签至少出现50次。此外，如果测试集包含许多密切相关的实例——例如来自一个单独文档中的实例——那么测试集的大小应增加，以确保这种多样性的缺乏不会扭曲评估结果。当有大量已标注数据可用时，只使用整体数据的10％进行评估常常会在安全方面犯错。\n",
    "\n",
    "选择测试集时另一个需要考虑的是测试集中实例与开发集中的实例的相似程度。这两个数据集越相似，我们对将评估结果推广到其他数据集的信心就越小。例如，考虑词性标注任务。在一种极端情况，我们可以通过从一个反映单一的文体（如新闻）的数据源随机分配句子，创建训练集和测试集："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6d2ba738",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from nltk.corpus import brown\n",
    "tagged_sents = list(brown.tagged_sents(categories='news'))\n",
    "random.shuffle(tagged_sents)\n",
    "size = int(len(tagged_sents) * 0.1)\n",
    "train_set, test_set = tagged_sents[size:], tagged_sents[:size]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ea9c9c",
   "metadata": {},
   "source": [
    "在这种情况下，我们的测试集和训练集将是*非常*相似的。训练集和测试集均取自同一文体，所以我们不能相信评估结果可以推广到其他文体。更糟糕的是，因为调用`random.shuffle()`，测试集中包含来自训练使用过的相同的文档的句子。如果文档中有相容的模式（也就是说，如果一个给定的词与特定词性标记一起出现特别频繁），那么这种差异将体现在开发集和测试集。一个稍好的做法是确保训练集和测试集来自不同的文件："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7b34b469",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_ids = brown.fileids(categories='news')\n",
    "size = int(len(file_ids) * 0.1)\n",
    "train_set = brown.tagged_sents(file_ids[size:])\n",
    "test_set = brown.tagged_sents(file_ids[:size])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fa5d445",
   "metadata": {},
   "source": [
    "如果我们要执行更令人信服的评估，可以从与训练集中文档联系更少的文档中获取测试集："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "55ec2963",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_set = brown.tagged_sents(categories='news') # 原始代码获取训练集和测试集格式错误\n",
    "# test_set = brown.tagged_sents(categories='fiction')\n",
    "\n",
    "train_sents = brown.tagged_sents(categories=['news', 'editorial'])\n",
    "test_sents = brown.tagged_sents(categories=['fiction', 'humor'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80c0e23",
   "metadata": {},
   "source": [
    "如果我们在此测试集上建立了一个性能很好的分类器，那么我们完全可以相信它有能力很好的泛化到用于训练它的数据以外的数据。\n",
    "\n",
    "## 3.2 准确度\n",
    "\n",
    "用于评估一个分类最简单的度量是准确度，测量测试集上分类器正确标注的输入的比例。例如，一个名字性别分类器，在包含80个名字的测试集上预测正确的名字有60个，它有60/80 = 75％的准确度。`nltk.classify.accuracy()`函数会在给定的测试集上计算分类器模型的准确度："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "cf0e6951",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.00\n"
     ]
    }
   ],
   "source": [
    "# 构建特征提取器\n",
    "def pos_features(sentence, i):\n",
    "    features = {'suffix(1)': sentence[i][-1:],\n",
    "                'suffix(2)': sentence[i][-2:],\n",
    "                'suffix(3)': sentence[i][-3:]}\n",
    "    if i == 0:\n",
    "        features['prev-word'] = '<START>'\n",
    "    else:\n",
    "        features['prev-word'] = sentence[i-1]\n",
    "    return features\n",
    "\n",
    "# 构建训练集和测试集\n",
    "train_set = [(pos_features(sent, i), tag) \n",
    "             for sent in train_sents \n",
    "             for i, (word, tag) in enumerate(sent)]\n",
    "test_set = [(pos_features(sent, i), tag) \n",
    "            for sent in test_sents \n",
    "            for i, (word, tag) in enumerate(sent)]\n",
    "\n",
    "# 训练并评估分类器\n",
    "classifier = nltk.NaiveBayesClassifier.train(train_set)\n",
    "print('Accuracy: {:4.2f}'.format(nltk.classify.accuracy(classifier, test_set)))"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "id": "60b5528a",
   "metadata": {},
   "source": [
    "解释一个分类器的准确性得分，考虑测试集中单个类标签的频率是很重要的。例如，考虑一个决定词bank每次出现的正确的词意的分类器。如果我们在金融新闻文本上评估分类器，那么我们可能会发现，`金融机构`的意思20个里面出现了19次。在这种情况下，95％的准确度也难以给人留下深刻印象，因为我们可以实现一个模型，它总是返回`金融机构`的意义。然而，如果我们在一个更加平衡的语料库上评估分类器，那里的最频繁的词意只占40％，那么95％的准确度得分将是一个更加积极的结果。（在[2](https://usyiyi.github.io/nlp-py-2e-zh/ch11.html#sec-life-cycle-of-a-corpus)测量标注一致性程度时也会有类似的问题。）\n",
    "\n",
    "## 3.3 精确度和召回率\n",
    "\n",
    "另一个准确度分数可能会产生误导的实例是在“搜索”任务中，如信息检索，我们试图找出与特定任务有关的文档。由于不相关的文档的数量远远多于相关文档的数量，一个将每一个文档都标记为无关的模型的准确度分数将非常接近100％。\n",
    "\n",
    "![e591e60c490795add5183c998132ebc0.png](attachment:e591e60c490795add5183c998132ebc0.png)\n",
    "\n",
    "图 3.1：真与假的阳性和阴性\n",
    "\n",
    "因此，对搜索任务使用不同的测量集是很常见的，基于[3.1](https://usyiyi.github.io/nlp-py-2e-zh/6.html#fig-precision-recall)所示的四个类别的每一个中的项目的数量：\n",
    "\n",
    "- 真阳性是相关项目中我们正确识别为相关的。\n",
    "- 真阴性是不相关项目中我们正确识别为不相关的。\n",
    "- 假阳性（或I 型错误）是不相关项目中我们错误识别为相关的。\n",
    "- 假阴性（或II型错误）是相关项目中我们错误识别为不相关的。\n",
    "\n",
    "给定这四个数字，我们可以定义以下指标：\n",
    "\n",
    "- 精确度，表示我们发现的项目中有多少是相关的，TP/(TP+FP)。\n",
    "- 召回率，表示相关的项目中我们发现了多少，TP/(TP+FN)。\n",
    "- F-度量值（或F-Score），组合精确度和召回率为一个单独的得分，被定义为精确度和召回率的调和平均数(2 × *Precision* × *Recall*) / (*Precision* + *Recall*)。\n",
    "\n",
    "## 3.4 混淆矩阵\n",
    "\n",
    "当处理有3 个或更多的标签的分类任务时，基于模型错误类型细分模型的错误是有信息量的。一个混淆矩阵是一个表，其中每个cells [i,j]表示正确的标签i被预测为标签j的次数。因此，对角线项目（即cells [|ii|](https://usyiyi.github.io/nlp-py-2e-zh/6.html#id17)）表示正确预测的标签，非对角线项目表示错误。在下面的例子中，我们为[4](https://usyiyi.github.io/nlp-py-2e-zh/5.html#sec-automatic-tagging)中开发的一元标注器生成一个混淆矩阵："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0817113a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tag_list(tagged_sents):\n",
    "    return [tag for sent in tagged_sents for (word, tag) in sent] # 该块原始代码有误，请参考下一个块的代码\n",
    "def apply_tagger(tagger, corpus):\n",
    "    return [tagger.tag(nltk.tag.untag(sent)) for sent in corpus]\n",
    "gold = tag_list(brown.tagged_sents(categories='editorial'))\n",
    "test = tag_list(apply_tagger(t2, brown.tagged_sents(categories='editorial')))\n",
    "cm = nltk.ConfusionMatrix(gold, test)\n",
    "print(cm.pretty_format(sort_by_count=True, show_percents=True, truncate=9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fe6344ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    |                                         N                      |\n",
      "    |      N      I      A      J             N             V      N |\n",
      "    |      N      N      T      J      .      S      ,      B      P |\n",
      "----+----------------------------------------------------------------+\n",
      " NN | <11.9%>  0.0%      .   0.2%      .   0.0%      .   0.2%   0.0% |\n",
      " IN |   0.0%  <9.0%>     .      .      .   0.0%      .      .      . |\n",
      " AT |      .      .  <8.6%>     .      .      .      .      .      . |\n",
      " JJ |   1.6%      .      .  <4.0%>     .      .      .   0.0%   0.0% |\n",
      "  . |      .      .      .      .  <4.8%>     .      .      .      . |\n",
      "NNS |   1.5%      .      .      .      .  <3.3%>     .      .   0.0% |\n",
      "  , |      .      .      .      .      .      .  <4.4%>     .      . |\n",
      " VB |   0.9%      .      .   0.0%      .      .      .  <2.4%>     . |\n",
      " NP |   1.0%      .      .   0.0%      .      .      .      .  <1.9%>|\n",
      "----+----------------------------------------------------------------+\n",
      "(row = reference; col = test)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "# 加载训练好的模型\n",
    "with open(\"t2.pkl\", \"rb\") as f:\n",
    "    t2 = pickle.load(f)\n",
    "\n",
    "# 定义辅助函数\n",
    "def tag_list(tagged_sents):\n",
    "    return [tag for sent in tagged_sents for (word, tag) in sent]\n",
    "\n",
    "def apply_tagger(tagger, corpus):\n",
    "    return [tagger.tag(nltk.tag.untag(sent)) for sent in corpus]\n",
    "\n",
    "# 对测试集进行标注\n",
    "gold = tag_list(brown.tagged_sents(categories='editorial'))\n",
    "test = tag_list(apply_tagger(t2, brown.tagged_sents(categories='editorial')))\n",
    "\n",
    "# 计算混淆矩阵并打印结果\n",
    "cm = nltk.ConfusionMatrix(gold, test)\n",
    "print(cm.pretty_format(sort_by_count=True, show_percents=True, truncate=9))"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "568ba96e",
   "metadata": {},
   "source": [
    "[](KCpe4pa9XiopIENoYXRHUFQg5aSq5aW955So5LqG)这个混淆矩阵显示常见的错误，包括`NN`替换为了`JJ`（1.6%的词），`NN`替换为了`NNS`（1.5%的词）。注意点（`.`）表示值为0 的单元格，对角线项目——对应正确的分类——用尖括号标记。.. XXX explain use of \"reference\" in the legend above.\n",
    "\n",
    "## 3.5 交叉验证\n",
    "\n",
    "为了评估我们的模型，我们必须为测试集保留一部分已标注的数据。正如我们已经提到，如果测试集是太小了，我们的评价可能不准确。然而，测试集设置较大通常意味着训练集设置较小，如果已标注数据的数量有限，这样设置对性能会产生重大影响。\n",
    "\n",
    "这个问题的解决方案之一是在不同的测试集上执行多个评估，然后组合这些评估的得分，这种技术被称为交叉验证。特别是，我们将原始语料细分为N个子集称为折叠。对于每一个这些的折叠，我们使用*除*这个折叠中的数据外其他所有数据训练模型，然后在这个折叠上测试模型。即使个别的折叠可能是太小了而不能在其上给出准确的评价分数，综合评估得分是基于大量的数据，因此是相当可靠的。\n",
    "\n",
    "第二，同样重要的，采用交叉验证的优势是，它可以让我们研究不同的训练集上性能变化有多大。如果我们从所有N个训练集得到非常相似的分数，然后我们可以相当有信心，得分是准确的。另一方面，如果N个训练集上分数很大不同，那么，我们应该对评估得分的准确性持怀疑态度。\n",
    "\n",
    "## 4 决策树\n",
    "\n",
    "接下来的三节中，我们将仔细看看可用于自动生成分类模型的三种机器学习方法：决策树、朴素贝叶斯分类器和最大熵分类器。正如我们所看到的，可以把这些学习方法看作黑盒子，直接训练模式，使用它们进行预测而不需要理解它们是如何工作的。但是，仔细看看这些学习方法如何基于一个训练集上的数据选择模型，会学到很多。了解这些方法可以帮助指导我们选择相应的特征，尤其是我们关于那些特征如何编码的决定。理解生成的模型可以让我们更好的提取信息，哪些特征对有信息量，那些特征之间如何相互关联。\n",
    "\n",
    "决策树是一个简单的为输入值选择标签的流程图。这个流程图由检查特征值的决策节点和分配标签的叶节点组成。为输入值选择标签，我们以流程图的初始决策节点开始，称为其根节点。此节点包含一个条件，检查输入值的特征之一，基于该特征的值选择一个分支。沿着这个描述我们输入值的分支，我们到达了一个新的决策节点，有一个关于输入值的特征的新的条件。我们继续沿着每个节点的条件选择的分支，直到到达叶节点，它为输入值提供了一个标签。[4.1](https://usyiyi.github.io/nlp-py-2e-zh/6.html#fig-decision-tree)显示名字性别任务的决策树模型的例子。\n",
    "\n",
    "![1ab45939cc9babb242ac45ed03a94f7a.png](attachment:1ab45939cc9babb242ac45ed03a94f7a.png)\n",
    "\n",
    "图 4.1：名字性别任务的决策树模型。请注意树图按照习惯画出“颠倒的”，根在上面，叶子在下面。\n",
    "\n",
    "一旦我们有了一个决策树，就可以直接用它来分配标签给新的输入值。不那么直接的是我们如何能够建立一个模拟给定的训练集的决策树。但在此之前，我们看一下建立决策树的学习算法，思考一个简单的任务：为语料库选择最好的“决策树桩”。决策树桩是只有一个节点的决策树，基于一个特征决定如何为输入分类。每个可能的特征值一个叶子，为特征有那个值的输入指定类标签。要建立决策树桩，我们首先必须决定哪些特征应该使用。最简单的方法是为每个可能的特征建立一个决策树桩，看哪一个在训练数据上得到最高的准确度，也有其他的替代方案，我们将在下面讨论。一旦我们选择了一个特征，就可以通过分配一个标签给每个叶子，基于在训练集中所选的例子的最频繁的标签，建立决策树桩（即选择特征具有那个值的例子）。\n",
    "\n",
    "给出了选择决策树桩的算法，生长出较大的决策树的算法就很简单了。首先，我们选择分类任务的整体最佳的决策树桩。然后，我们在训练集上检查每个叶子的准确度。没有达到足够的准确度的叶片被新的决策树桩替换，新决策树桩是在根据到叶子的路径选择的训练语料的子集上训练的。例如，我们可以使[4.1](https://usyiyi.github.io/nlp-py-2e-zh/6.html#fig-decision-tree)中的决策树生长，通过替换最左边的叶子为新的决策树桩，这个新的决策树桩是在名字不以\"k\"开始或以一个元音或\"l\"结尾的训练集的子集上训练的。\n",
    "\n",
    "<a href=\"#41-熵和信息增益\">4.1 熵和信息增益</a> \n",
    "\n",
    "## 4.1 熵和信息增益\n",
    "\n",
    "正如之前提到的，有几种方法来为决策树桩确定最有信息量的特征。一种流行的替代方法，被称为信息增益，当我们用给定的特征分割输入值时，衡量它们变得更有序的程度。要衡量原始输入值集合如何无序，我们计算它们的标签的墒，如果输入值的标签非常不同，墒就高；如果输入值的标签都相同，墒就低。特别地，熵被定义为每个标签的概率乘以那个标签的log概率的总和："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "597e8ff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "def entropy(labels):\n",
    "    freqdist = nltk.FreqDist(labels)\n",
    "    probs = [freqdist.freq(l) for l in freqdist]\n",
    "    return -sum(p * math.log(p,2) for p in probs)"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "4c843bf5",
   "metadata": {},
   "source": [
    "一旦我们已经计算了原始输入值的标签集的墒，就可以判断应用了决策树桩之后标签会变得多么有序。为了这样做，我们计算每个决策树桩的叶子的熵，利用这些叶子熵值的平均值（加权每片叶子的样本数量）。信息增益等于原来的熵减去这个新的减少的熵。信息增益越高，将输入值分为相关组的决策树桩就越好，于是我们可以通过选择具有最高信息增益的决策树桩来建立决策树。\n",
    "\n",
    "决策树的另一个考虑因素是效率。前面描述的选择决策树桩的简单算法必须为每一个可能的特征构建候选决策树桩，并且这个过程必须在构造决策树的每个节点上不断重复。已经开发了一些算法通过存储和重用先前评估的例子的信息减少训练时间。\n",
    "\n",
    "决策树有一些有用的性质。首先，它们简单明了，容易理解。决策树顶部附近尤其如此，这通常使学习算法可以找到非常有用的特征。决策树特别适合有很多层次的分类区别的情况。例如，决策树可以非常有效地捕捉进化树。\n",
    "\n",
    "然而，决策树也有一些缺点。一个问题是，由于决策树的每个分支会划分训练数据，在训练树的低节点，可用的训练数据量可能会变得非常小。因此，这些较低的决策节点可能\n",
    "\n",
    "过拟合训练集，学习模式反映训练集的特质而不是问题底层显著的语言学模式。对这个问题的一个解决方案是当训练数据量变得太小时停止分裂节点。另一种方案是长出一个完整的决策树，但随后剪去在开发测试集上不能提高性能的决策节点。\n",
    "\n",
    "决策树的第二个问题是，它们强迫特征按照一个特定的顺序进行检查，即使特征可能是相对独立的。例如，按主题分类文档（如体育、汽车或谋杀之谜）时，特征如`hasword(football)`极可能表示一个特定标签，无论其他的特征值是什么。由于决策树顶部附近的空间有限，大部分这些特征将需要在树中的许多不同的分支中重复。因为越往树的下方，分支的数量成指数倍增长，重复量可能非常大。\n",
    "\n",
    "一个相关的问题是决策树不善于利用对正确的标签具有较弱预测能力的特征。由于这些特征的影响相对较小，它们往往出现在决策树非常低的地方。决策树学习的时间远远不够用到这些特征，也不能留下足够的训练数据来可靠地确定它们应该有什么样的影响。如果我们能够在整个训练集中看看这些特征的影响，那么我们也许能够做出一些关于它们是如何影响标签的选择的结论。\n",
    "\n",
    "决策树需要按一个特定的顺序检查特征的事实，限制了它们的利用相对独立的特征的能力。我们下面将讨论的朴素贝叶斯分类方法克服了这一限制，允许所有特征“并行”的起作用。\n",
    "\n",
    "## 5 朴素贝叶斯分类器\n",
    "\n",
    "在朴素贝叶斯分类器中，每个特征都得到发言权，来确定哪个标签应该被分配到一个给定的输入值。为一个输入值选择标签，朴素贝叶斯分类器以计算每个标签的先验概率开始，它由在训练集上检查每个标签的频率来确定。之后，每个特征的贡献与它的先验概率组合，得到每个标签的似然估计。似然估计最高的标签会分配给输入值。[5.1](https://usyiyi.github.io/nlp-py-2e-zh/6.html#fig-naive-bayes-triangle)说明了这一过程。\n",
    "\n",
    "![fff90c564d2625f739b442b23301906e.png](attachment:fff90c564d2625f739b442b23301906e.png)\n",
    "\n",
    "图 5.1：使用朴素贝叶斯分类器为文档选择主题的程序的抽象图解。在训练语料中，大多数文档是有关汽车的，所以分类器从接近“汽车”的标签的点上开始。但它会考虑每个特征的影响。在这个例子中，输入文档中包含的词\"dark\"，它是谋杀之谜的一个不太强的指标，也包含词\"football\"，它是体育文档的一个有力指标。每个特征都作出了贡献之后，分类器检查哪个标签最接近，并将该标签分配给输入。\n",
    "\n",
    "个别特征对整体决策作出自己的贡献，通过“投票反对”那些不经常出现的特征的标签。特别是，每个标签的似然得分由于与输入值具有此特征的标签的概率相乘而减小。例如，如果词run在12%的体育文档中出现，在10%的谋杀之谜的文档中出现，在2％的汽车文档中出现，那么体育标签的似然得分将被乘以0.12，谋杀之谜标签将被乘以0.1，汽车标签将被乘以0.02。整体效果是略高于体育标签的得分的谋杀之谜标签的得分会减少，而汽车标签相对于其他两个标签会显著减少。这个过程如[5.2](https://usyiyi.github.io/nlp-py-2e-zh/6.html#fig-naive-bayes-bargraph)和[5.3](https://usyiyi.github.io/nlp-py-2e-zh/6.html#fig-naive-bayes-graph)所示。"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "fb5e6b1b",
   "metadata": {},
   "source": [
    "![b502c97e1f935240559d38b397805b32.png](attachment:b502c97e1f935240559d38b397805b32.png)\n",
    "\n",
    "图 5.2：计算朴素贝叶斯的标签似然得分。朴素贝叶斯以计算每个标签的先验概率开始，基于每个标签出现在训练数据中的频率。然后每个特征都用于估计每个标签的似然估计，通过用输入值中有那个特征的标签的概率乘以它。似然得分结果可以认为是从具有给定的标签和特征集的训练集中随机选取的值的概率的估计，假设所有特征概率是独立的。\n",
    "\n",
    "<a href=\"#51-底层的概率模型\">5.1 底层的概率模型</a>\n",
    "\n",
    "## 5.1 底层的概率模型\n",
    "\n",
    "理解朴素贝叶斯分类器的另一种方式是它为输入选择最有可能的标签，基于下面的假设：每个输入值是通过首先为那个输入值选择一个类标签，然后产生每个特征的方式产生的，每个特征与其他特征完全独立。当然，这种假设是不现实的，特征往往高度依赖彼此。我们将在本节结尾回过来讨论这个假设的一些后果。这简化的假设，称为朴素贝叶斯假设（或独立性假设），使得它更容易组合不同特征的贡献，因为我们不必担心它们相互影响。\n",
    "\n"
   ]
  },
  {
   "attachments": {
    "3edaf7564caaab7c8ddc83db1d5408a3.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "432294d2",
   "metadata": {},
   "source": [
    "![3edaf7564caaab7c8ddc83db1d5408a3.png](attachment:3edaf7564caaab7c8ddc83db1d5408a3.png)\n",
    "\n",
    "图 5.3：一个贝叶斯网络图演示朴素贝叶斯分类器假定的生成过程。要生成一个标记的输入，模型先为输入选择标签，然后基于该标签生成每个输入的特征。对给定标签，每个特征被认为是完全独立于所有其他特征的。\n",
    "\n",
    "基于这个假设，我们可以计算表达式P(label|features)，给定一个特别的特征集一个输入具有特定标签的概率。要为一个新的输入选择标签，我们可以简单地选择使P(l|features)最大的标签l。\n",
    "\n",
    "一开始，我们注意到P(label|features)等于具有特定标签*和*特定特征集的输入的概率除以具有特定特征集的输入的概率："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6850329c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import senseval\n",
    "instances = senseval.instances('hard.pos')\n",
    "size = int(len(instances) * 0.1)\n",
    "train_set, test_set = instances[size:], instances[:size]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2567233d",
   "metadata": {},
   "source": [
    "使用这个数据集，建立一个分类器，预测一个给定的实例的正确的词意标签。关于使用Senseval 2 语料库返回的实例对象的信息请参阅`http://nltk.org/howto`上的语料库HOWTO。\n",
    "\n",
    "☼ 使用本章讨论过的电影评论文档分类器，产生对分类器最有信息量的30个特征的列表。你能解释为什么这些特定特征具有信息量吗？你能在它们中找到什么惊人的发现吗？\n",
    "\n",
    "☼ 选择一个本章所描述的分类任务，如名字性别检测、文档分类、词性标注或对话行为分类。使用相同的训练和测试数据，相同的特征提取器，建立该任务的三个分类器：：决策树、朴素贝叶斯分类器和最大熵分类器。比较你所选任务上这三个分类器的准确性。你如何看待如果你使用了不同的特征提取器，你的结果可能会不同？\n",
    "\n",
    "☼ 同义词strong和powerful的模式不同（尝试将它们与chip和sales结合）。哪些特征与这种区别有关？建立一个分类器，预测每个词何时该被使用。\n",
    "\n",
    "◑ 对话行为分类器为每个帖子分配标签，不考虑帖子的上下文背景。然而，对话行为是高度依赖上下文的，一些对话行序列可能比别的更相近。例如，ynQuestion 对话行为更容易被一个`yanswer`回答而不是以一个`问候`来回答。利用这一事实，建立一个连续的分类器，为对话行为加标签。一定要考虑哪些特征可能是有用的。参见[1.7](https://usyiyi.github.io/nlp-py-2e-zh/6.html#code-consecutive-pos-tagger)词性标记的连续分类器的代码，获得一些想法。\n",
    "\n",
    "◑ 词特征在处理文本分类中是非常有用的，因为在一个文档中出现的词对于其语义内容是什么具有强烈的指示作用。然而，很多词很少出现，一些在文档中的最有信息量的词可能永远不会出现在我们的训练数据中。一种解决方法是使用一个词典，它描述了词之间的不同。使用WordNet词典，加强本章介绍的电影评论文档分类器，使用概括一个文档中出现的词的特征，使之更容易匹配在训练数据中发现的词。\n",
    "\n",
    "★ PP 附件语料库是描述介词短语附着决策的语料库。语料库中的每个实例被编码为`PPAttachment`对象："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6dd5471f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PPAttachment(sent='0', verb='join', noun1='board', prep='as', noun2='director', attachment='V'), PPAttachment(sent='1', verb='is', noun1='chairman', prep='of', noun2='N.V.', attachment='N'), ...]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import ppattach\n",
    "ppattach.attachments('training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c423cb6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('chairman', 'of', 'N.V.')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inst = ppattach.attachments('training')[1]\n",
    "(inst.noun1, inst.prep, inst.noun2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a68562",
   "metadata": {},
   "source": [
    "选择`inst.attachment`为`N`的唯一实例："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "40333b63",
   "metadata": {},
   "outputs": [],
   "source": [
    "nattach = [inst for inst in ppattach.attachments('training')\n",
    "    if inst.attachment == 'N']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0982cba6",
   "metadata": {},
   "source": [
    "使用此子语料库，建立一个分类器，尝试预测哪些介词是用来连接一对给定的名词。例如，给定的名词对\"team\"和\"researchers\"，分类器应该预测出介词\"of\"。更多的使用PP附件语料库的信息，参阅`http://nltk.org/howto`上的语料库HOWTO。\n",
    "\n",
    "★ 假设你想自动生成一个场景的散文描述，每个实体已经有了一个唯一描述此实体的词，例如the jar，只是想决定在有关的各项目中是否使用in或on，例如the book is in the cupboard对比the book is on the shelf。通过查找语料数据探讨这个问题；编写需要的程序。\n",
    "\n",
    "\n",
    "\n",
    "(13) a.in the car *versus* on the trainb.in town *versus* on campusc.in the picture *versus* on the screend.in Macbeth *versus* on Letterman\n",
    "\n",
    "关于本文档...\n",
    "\n",
    "针对NLTK 3.0 作出更新。本章来自于*Natural Language Processing with Python*，[Steven Bird](http://estive.net/), [Ewan Klein](http://homepages.inf.ed.ac.uk/ewan/) 和[Edward Loper](http://ed.loper.org/)，Copyright © 2014 作者所有。本章依据*Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License* [http://creativecommons.org/licenses/by-nc-nd/3.0/us/] 条款，与*自然语言工具包* [`http://nltk.org/`] 3.0 版一起发行。\n",
    "\n",
    "本文档构建于星期三 2015 年 7 月 1 日 12:30:05 AEST\n",
    "\n",
    "## Docutils System Messages\n",
    "\n",
    "System Message: ERROR/3 (`6.rst2`, line 1264); *[backlink](https://usyiyi.github.io/nlp-py-2e-zh/6.html#id18)*\n",
    "\n",
    "Undefined substitution referenced: \"ii\"."
   ]
  }
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